This repository performs automated parameter optimization to maximize calcium Ca2+ influx in the trunk of a CA1 pyramidal neuron model while minimizing calcium influx in oblique dendrites, using the Optuna framework for hyperparameter tuning.

The pipeline simulates dendritic currents using a custom CurrentscapePipeline, then evaluates performance via:

• trunk_score(): quantifies Ca2+ current in the distal trunk
• oblique_penalty(): penalizes Ca2+ current in oblique branches

The goal is to maximize trunk Ca2+ influx while suppressing unwanted oblique branch activation.

• main_full_pipeline.py: orchestrates the optimization loop
• CurrentscapePipeline.py: defines neuron model and simulation logic
• custom_objective_scores.py: defines custom scoring functions

Use Python 3.9+ and install dependencies with:

pip install -r requirements.txt

Before running simulations, you need to compile NEURON's .mod files. Navigate to the folder containing the .mod files:

cd simulator/model/density_mechs

And run the nrnivmodl tool from NEURON.

If NEURON has trouble finding compiled mechanisms, consider copying them into the root folder and re-running nrnivmodl.

More information about NEURON: https://neuron.yale.edu/neuron
More information about working with .mod files: https://www.neuron.yale.edu/phpBB/viewtopic.php?t=3263
More information about compiling .mod files: https://nrn.readthedocs.io/en/latest/guide/faq.html#how-do-i-compile-mod-files

1. Suggest Parameters:
   • gcar, gkslow, gcar_trunk, and gkslow_trunk are optimized.
2. Run Simulation:
   • A current injection protocol is simulated via CurrentscapePipeline.
3. Score Simulation:
   • High trunk_score → rewarded
   • High oblique_penalty → penalized
4. Optimize:
   • Optuna runs 50 trials to maximize the objective.
5. Visualization:
   • Configure inputs and run visualization_optuna.py to visualize the top 5 scoring parameter sets.